Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.

中文翻译：

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基于视频识别的虚拟现实中3D人体动作姿势捕捉

运动姿势捕捉技术可以有效地解决3D动画制作过程中定义角色运动的难题，大大减少了角色运动控制的工作量，从而提高了动画开发的效率和角色运动的逼真度。运动手势捕获技术被广泛用于虚拟现实系统，虚拟训练场以及实时跟踪普通物体的运动轨迹。提出了一种适合嵌入的姿态估计算法。先前的集中式卡尔曼滤波分为两步式卡尔曼滤波。根据传感器的不同特性，将它们分别处理以隔离传感器之间的交叉影响。提出了一种基于模糊逻辑的自适应调整方法。加速度 角速度和环境的地磁场强度用作模糊逻辑的输入，以判断载体的运动状态，然后调整滤波器的协方差矩阵。传感器的自适应调整被转换为运动状态的识别。为了研究人体运动姿势捕捉，本文基于实验室中现有的机械臂设计了验证实验。实验表明，所研究的运动姿态捕获方法具有较好的性能。该人体运动手势是为捕获实验而设计的，捕获结果表明，所获得的姿态角信息可以更好地恢复人体运动。建立了人体运动姿势捕捉的视觉模型，并与实际情况进行比较分析后，结果发现，仿真方法很好地再现了人体运动的运动过程。为了研究人体运动识别，本文设计了两种分类模型和人类日常行为进行实验。实验表明，两类人体运动手势的捕获和识别精度均取得了良好的效果。SVC对两种分类的识别的实验效果非常好。在使用所有优化算法的情况下，准确率均高于90％，最终识别准确率也高于90％。就识别时间而言，人体运动手势捕获和识别所需的时间少于2 s。本文设计了两种分类模型和人类日常行为进行实验。实验表明，两类人体运动手势的捕获和识别精度均取得了良好的效果。SVC对两种分类的识别的实验效果非常好。在使用所有优化算法的情况下，准确率均高于90％，最终识别准确率也高于90％。就识别时间而言，人体运动手势捕获和识别所需的时间少于2 s。本文设计了两种分类模型和人类日常行为进行实验。实验表明，两类人体运动手势的捕捉和识别精度均取得了良好的效果。SVC对两种分类的识别的实验效果非常好。在使用所有优化算法的情况下，准确率均高于90％，最终识别准确率也高于90％。就识别时间而言，人体运动手势捕获和识别所需的时间少于2 s。在使用所有优化算法的情况下，准确率均高于90％，最终识别准确率也高于90％。就识别时间而言，人体运动手势捕获和识别所需的时间少于2 s。在使用所有优化算法的情况下，准确率均高于90％，最终识别准确率也高于90％。就识别时间而言，人体运动手势捕获和识别所需的时间少于2 s。